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https://gitlab.com/libeigen/eigen.git
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00f32752f7
* Unifying all loadLocalTile from lhs and rhs to an extract_block function. * Adding get_tensor operation which was missing in TensorContractionMapper. * Adding the -D method missing from cmake for Disable_Skinny Contraction operation. * Wrapping all the indices in TensorScanSycl into Scan parameter struct. * Fixing typo in Device SYCL * Unifying load to private register for tall/skinny no shared * Unifying load to vector tile for tensor-vector/vector-tensor operation * Removing all the LHS/RHS class for extracting data from global * Removing Outputfunction from TensorContractionSkinnyNoshared. * Combining the local memory version of tall/skinny and normal tensor contraction into one kernel. * Combining the no-local memory version of tall/skinny and normal tensor contraction into one kernel. * Combining General Tensor-Vector and VectorTensor contraction into one kernel. * Making double buffering optional for Tensor contraction when local memory is version is used. * Modifying benchmark to accept custom Reduction Sizes * Disabling AVX optimization for SYCL backend on the host to allow SSE optimization to the host * Adding Test for SYCL * Modifying SYCL CMake
63 lines
2.5 KiB
C++
63 lines
2.5 KiB
C++
#include <iostream>
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#define EIGEN_USE_SYCL
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#include <unsupported/Eigen/CXX11/Tensor>
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using Eigen::array;
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using Eigen::SyclDevice;
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using Eigen::Tensor;
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using Eigen::TensorMap;
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int main()
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{
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using DataType = float;
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using IndexType = int64_t;
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constexpr auto DataLayout = Eigen::RowMajor;
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auto devices = Eigen::get_sycl_supported_devices();
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const auto device_selector = *devices.begin();
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Eigen::QueueInterface queueInterface(device_selector);
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auto sycl_device = Eigen::SyclDevice(&queueInterface);
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// create the tensors to be used in the operation
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IndexType sizeDim1 = 3;
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IndexType sizeDim2 = 3;
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IndexType sizeDim3 = 3;
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array<IndexType, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
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// initialize the tensors with the data we want manipulate to
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Tensor<DataType, 3,DataLayout, IndexType> in1(tensorRange);
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Tensor<DataType, 3,DataLayout, IndexType> in2(tensorRange);
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Tensor<DataType, 3,DataLayout, IndexType> out(tensorRange);
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// set up some random data in the tensors to be multiplied
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in1 = in1.random();
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in2 = in2.random();
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// allocate memory for the tensors
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DataType * gpu_in1_data = static_cast<DataType*>(sycl_device.allocate(in1.size()*sizeof(DataType)));
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DataType * gpu_in2_data = static_cast<DataType*>(sycl_device.allocate(in2.size()*sizeof(DataType)));
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DataType * gpu_out_data = static_cast<DataType*>(sycl_device.allocate(out.size()*sizeof(DataType)));
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//
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TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_in1(gpu_in1_data, tensorRange);
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TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_in2(gpu_in2_data, tensorRange);
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TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_out(gpu_out_data, tensorRange);
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// copy the memory to the device and do the c=a*b calculation
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sycl_device.memcpyHostToDevice(gpu_in1_data, in1.data(),(in1.size())*sizeof(DataType));
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sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(),(in2.size())*sizeof(DataType));
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gpu_out.device(sycl_device) = gpu_in1 * gpu_in2;
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sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.size())*sizeof(DataType));
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sycl_device.synchronize();
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// print out the results
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for (IndexType i = 0; i < sizeDim1; ++i) {
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for (IndexType j = 0; j < sizeDim2; ++j) {
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for (IndexType k = 0; k < sizeDim3; ++k) {
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std::cout << "device_out" << "(" << i << ", " << j << ", " << k << ") : " << out(i,j,k)
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<< " vs host_out" << "(" << i << ", " << j << ", " << k << ") : " << in1(i,j,k) * in2(i,j,k) << "\n";
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}
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}
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}
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printf("c=a*b Done\n");
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} |